Non-regular electrical stimulation patterns for treating neurological disorders

ABSTRACT

Systems and methods for stimulation of neurological tissue and generation stimulation trains with temporal patterns of stimulation, in which the interval between electrical pulses (the inter-pulse intervals) changes or varies over time. The features of the stimulation trains may be selected and arranged algorithmically to by clinical trial. These stimulation trains are generated to target a specific neurological disorder, by arranging sets of features which reduce symptoms of that neurological disorder into a pattern which is effective at reducing those symptoms while maintaining or reducing power consumption versus regular stimulation signals. Compared to conventional continuous, high rate pulse trains having regular (i.e., constant) inter-pulse intervals, the non-regular (i.e., not constant) pulse patterns or trains that embody features of the invention provide increased efficacy and/or a lower than average frequency.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/649,912, entitled “Non-Regular Electrical Stimulation Patterns ForTreating Neurological Disorders”, filed Oct. 11, 2012, which claims thebenefit of co-pending U.S. Provisional Patent Application Ser. No.61/558,871, filed Nov. 11, 2011, and entitled “Non-Regular ElectricalStimulation Patterns for Treating Neurological Disorders,” and alsoclaims the benefit of co-pending U.S. Provisional Patent ApplicationSer. No. 61/545,791, filed Oct. 11, 2011, and entitled “Non-RegularPatterns of Deep Brain Stimulation for the Suppression of NeurologicalDisorder Symptoms,” which was also a continuation-in-part of pendingU.S. patent application Ser. No. 12/587,295, filed Oct. 5, 2009, andentitled “Non-Regular Electrical Stimulation Patterns for TreatingNeurological Disorders,” which claims the benefit of U.S. ProvisionalPatent Application Ser. No. 61/102,575, filed Oct. 3, 2008; and entitled“Stimulation Patterns For Treating Neurological Disorders Via Deep BrainStimulation,” all of which are incorporated herein in their entirety byreference.

BACKGROUND OF THE INVENTION

Systems and methods according to the present invention relate generallyto neural stimulation in animals, including humans. Deep BrainStimulation (DBS) has been found to be successful in treating a varietyof neurological disorders, including movement disorders. High frequencyDBS in the internal segment of the globus pallidus (GPi) or subthalamicnucleus (STN) is an effective and adjustable surgical treatment formotor symptoms of advanced Parkinson's disease (PD). DBS reduces tremor,rigidity, akinesia, and postural instability, and allows levodopa dosesto be decreased. Patients clinically diagnosed with idiopathic PDsuffering from the cardinal motor symptoms are likely to receive benefitfrom DBS, with levodopa responsiveness predictive of its efficacy.Similarly, high frequency DBS in the ventral intermediate nucleus (Vim)of the thalamus is an effective and adjustable surgical treatment fortremor in persons with essential tremor or multiple sclerosis. As well,DBS is used to treat a broad range of neurological and psychiatricdisorders including but not limited to epilepsy, dystonia, obsessivecompulsive disorder, depression, Tourette's syndrome, addiction, andAlzheimer's disease.

Generally, such treatment involves placement of a DBS type lead into atargeted region of the brain through a burr hole drilled in thepatient's skull, and the application of appropriate stimulation throughthe lead to the targeted region.

Presently, in DBS, beneficial (symptom-relieving) effects are observedprimarily at high stimulation frequencies above 100 Hz that aredelivered in stimulation patterns or trains in which the intervalbetween electrical pulses (the inter-pulse intervals) is constant overtime. The trace of a conventional stimulation train for DBS is shown inFIG. 2. The beneficial effects of DBS on symptoms are only observed athigh frequencies, while low frequency stimulation may exacerbatesymptoms. Thalamic DBS at less than or equal to 50 Hz has been shown toincrease tremor in patients with essential tremor (ET). Similarly, 50 HzDBS has been shown to produce tremor in pain patients receivingsimulation of the ventral posterior medial nucleus of the thalamus(VPM), but the tremor disappears when the frequency is increased.Likewise, DBS of the subthalamic nucleus (STN) at 10 Hz has been shownto worsen akinesia in patients with while DBS at 130 Hz has been shownto improve motor function. Similarly, stimulation of the globus pallidus(GPi) at or above 130 Hz has been shown to improve dystonia, whereasstimulation at either 5 or 50 Hz leads to significant worsening.

In patients with ET, random patterns of stimulation are less effectiveat relieving tremor than regular patterns of stimulation. Similarly, inpatients with PD, random patterns of stimulation are less effective atrelieving bradykinesia than regular patterns of stimulation. In patientswith ET, non-regular stimulation patterns are less effective atsuppressing tremor than temporally regular stimulation becausesufficiently long gaps in the stimulation train allow pathologicalactivity to propagate through the stimulated nucleus. However, thefeatures of non-regular stimulation patterns that influence clinicalefficacy in PD are unknown.

Model studies also indicate that the masking of pathological burstactivity occurs only with sufficiently high stimulation frequencies.Responsiveness of tremor to changes in DBS amplitude and frequency arestrongly correlated with the ability of applied stimuli to mask neuronalbursting.

Although effective, conventional high frequency stimulation generatesstronger side-effects than low frequency stimulation, and thetherapeutic window between the voltage that generates the desiredclinical effect(s) and the voltage that generates undesired side effectsdecreases with increasing frequency. Precise lead placement thereforebecomes important. Further, high stimulation frequencies increase powerconsumption. The need for higher frequencies and increased powerconsumption shortens the useful lifetime and/or increases the physicalsize of battery-powered implantable pulse generators. The need forhigher frequencies and increased power consumption requires a largerbattery size, and frequent charging of the battery, if the battery isrechargeable. Thus, the art of DBS would benefit from systems andmethods having significantly increased efficacy over prior Regularstimulation while reducing, or minimizing impact on, battery life.

SUMMARY OF THE INVENTION

One aspect of the present invention is to provide a temporal pattern ofstimulation for application to targeted neurological tissue comprising arepeating succession of non-regular pulse trains, each pulse traincomprising a plurality of evenly spaced pulses and at least one pulsefeature.

Another aspect of the present invention is to provide a method ofgenerating series of stimulation signals for the treatment of aneurological disorder comprising: selecting a neurological disorder withone or more symptoms to be treated by the stimulation signals;identifying pulse features of the stimulation signals that suppress oneor more symptoms of the neurological disorder when applied to specificareas of a neurological tissue; selecting one or more patterns ofnon-regular stimulation signals comprised of the pulse features; andgenerating a pulse train of stimulation signals including the one ormore selected patterns.

An additional aspect of the invention is to provide a method forstimulation of a targeted neurological tissue region comprising applyinga non-regular pulse train, each pulse train comprising a plurality ofevenly spaced pulses and at least one pulse feature and repeating thepulse train in succession.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an anatomic view of a system for stimulating tissue of thecentral nervous system that includes an lead implanted in brain tissuecoupled to a pulse generator that is programmed to provide non-regular(i.e., not constant) pulse patterns or trains, in which the intervalbetween electrical pulses (the inter-pulse intervals) changes or variesover time.

FIG. 2 is a diagrammatic trace that shows a conventional regular highfrequency stimulation train, in which the interval between electricalpulses (the inter-pulse intervals) is constant.

FIG. 3 is a diagrammatic trace showing a representative example of arepeating non-regular pulse pattern or train in which the inter-pulseintervals are linearly cyclically ramped over time.

FIGS. 4 and 5 are diagrammatic traces showing other representativeexamples of repeating non-regular pulse patterns or trains comprisingwithin, a single pulse train, a combination of single pulses (singlets)and embedded multiple pulse groups (n-lets), with non-regularinter-pulse intervals between singlets and n-lets as well as non-regularinter-pulse intervals within the multiple pulse n-lets.

FIG. 6 depicts prior experimentation showing decreased efficacy inreducing symptoms as the variability of random patterns of DBSincreases, hick is modified from Dorval et al. (2010).

FIG. 7A depicts a “Uniform” stimulation pattern train according to thepresent invention.

FIG. 7B depicts a “Unipeak” stimulation train according to the presentinvention.

FIG. 7C depicts an “Absence” stimulation pattern train according to thepresent invention.

FIG. 7D depicts a “Presence” stimulation pattern train according to thepresent invention.

FIG. 8 is a table of stimulation pattern train parameters.

FIG. 9 is a table of patient data.

FIG. 10A is a timeline depicting stimulation response data collection.

FIG. 10B is a timeline depicting stimulation response data analysis.

FIG. 11 depicts prior stimulation experimentation establishing keydepression duration as being statistically significantly correlated tomotor symptom severity.

FIG. 12 depicts an exemplary embodiment of a portion of a stimulationresponse data collection system and associated method.

FIG. 13A depicts a histogram of click or button depression durations ofa patient.

FIG. 13B is a bar plot indicating a statistically significant perpatient finger effect.

FIG. 14 is a timeline of button depression or click durations for twopatients, one along the top line and one along the bottom line, whileDBS is off (left) and while DBS is on (right), respectively.

FIG. 15 is a bar graph showing statistically significant changes motorsymptom severity as assessed through the coefficient of variation ofclick duration across different temporal patterns of stimulationaccording to the present invention.

FIG. 16A depicts a generally accepted model used to generate thalamicneural responses to DBS and sensorimotor input (left) and types oferrors that may be generated by such model (right).

FIG. 16B depicts the DBS frequency-dependence of the model outcomemeasure, the error fraction, which mirrors the DBS frequency-dependenceof motor symptoms.

FIG. 17A is a graph of an average error fraction generated by the modelof FIG. 16A when presented with the different temporal patterns ofstimulation according to the present invention listed along the x-axis.

FIG. 17B is a graph of power of beta band oscillations in the GPIneurons of the model of FIG. 16A when presented with the differenttemporal patterns of stimulation according to the present inventionlisted along the x-axis.

FIG. 17C is a graph of the percentage of errors generated grouped bytype of error by the model of FIG. 16A when presented with the differenttemporal patterns of stimulation according to the present inventionlisted along the x-axis.

FIG. 18A is a graph of the log CV Duration during stimulation whenpresented with log CV Duration pre-stimulation listed along the x-axis.

FIG. 18B is a graph of the log CV Duration post-stimulation whenpresented with log CV Duration during stimulation listed along thex-axis.

FIG. 19A is a graph of the log CV Interval when presented with thedifferent temporal patterns of stimulation according to the presentinvention listed along the x-axis.

FIG. 19B is a graph of the log number of clicks when presented with thedifferent temporal patterns of stimulation according to the presentinvention listed along the x-axis.

FIG. 20 is a graph depicting prior stimulation experimentationestablishing log CV Duration as being statistically significantlycorrelated to UPDRS, or motor symptom severity.

FIG. 21A is a series of charts showing power density for the differenttemporal patterns of stimulation according to the present invention.

FIG. 21B is a graph showing beta band power for the different temporalpatterns of stimulation according to the present invention.

FIG. 21C is a graph depicting the log CV duration being correlated tobeta power.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Although the disclosure hereof is detailed and exact to enable thoseskilled in the art to practice the invention, the physical embodimentsherein disclosed merely exemplify the invention, which may be embodiedin other specific structures. While the preferred embodiment has beendescribed, the details may be changed without departing from theinvention, which is defined by the claims.

FIG. 1 is a system 10 for stimulating tissue of the central nervoussystem. The system includes a lead 12 placed in a desired position incontact with central nervous system tissue. In the illustratedembodiment, the lead 12 is implanted in a region of the brain, such asthe thalamus, subthalamus, or globus pallidus for the purpose of deepbrain stimulation. However, it should be understood, the lead 12 couldbe implanted in, on, or near the spinal cord; or in, on, or near aperipheral nerve (sensory or motor) for the purpose of selectivestimulation to achieve a therapeutic purpose.

The distal end of the lead 12 carries one or more electrodes 14 to applyelectrical pulses to the targeted tissue region. The electrical pulsesare supplied by a pulse generator 16 coupled to the lead 12.

In the illustrated embodiment, the pulse generator 16 is implanted in asuitable location remote from the lead 12, e.g., in the shoulder region.It should be appreciated, however, that the pulse generator 16 could beplaced in other regions of the body or externally.

When implanted, the case of the pulse generator can serve as a referenceor return electrode. Alternatively, the lead 12 can include a referenceor return electrode (comprising a bi-polar arrangement), or a separatereference or return electrode can be implanted or attached elsewhere onthe body (comprising a mono-polar arrangement).

The pulse generator 16 includes an on-board, programmable microprocessor18, which carries embedded code. The code expresses pre-programmed rulesor algorithms under which a desired electrical stimulation waveformpattern or train is generated and distributed to the electrode(s) 14 onthe lead 12. According to these programmed rules, the pulse generator 16directs the prescribed stimulation waveform patterns or trains throughthe lead 12 to the electrode(s) 14, which serve to stimulate selectivelythe targeted tissue region. The code is preprogrammed by a clinician toachieve the particular physiologic response desired.

In the illustrated embodiment, an on-board battery 20 supplies power tothe microprocessor 18. Currently, batteries 20 must be replaced every 1to 9 years, depending on the stimulation parameters needed to treat adisorder. When the battery life ends, the replacement of batteriesrequires another invasive surgical procedure to gain access to theimplanted pulse generator. As will be described, the system 10 makespossible, among its several benefits, an increase in battery life.

The stimulation waveform pattern or train generated by the pulsegenerator differs from convention pulse patterns or trains in that thetemporal pattern of stimulation comprises repeating non-regular (i.e.,not constant) pulse patterns or trains, in which the interval betweenelectrical pulses (the inter-pulse intervals or IPI) changes or variesover time. Examples of these repeating non-regular pulse patterns ortrains are shown in FIGS. 3 to 5. Compared to conventional pulse trainshaving regular (i.e., constant) inter-pulse intervals (as shown in FIG.2), the non-regular (i.e., not constant) pulse patterns or trainsprovide a lower average frequency for a given pulse pattern or train,where the average frequency for a given pulse train (expressed in hertzor Hz) is defined as the sum of the inter-pulse intervals for the pulsetrain in seconds (Σ_(IPI)) divided by the number of pulses (n) in thegiven pulse train, or (Σ_(IPI))/n. A lower average frequency makespossible a reduction in the intensity of side effects, as well as anincrease in the dynamic range between the onset of the desired clinicaleffect(s) and side effects, thereby increasing the clinical efficacy andreducing sensitivity to the position of the electrode(s). A loweraverage frequency brought about by a non-regular pulse pattern or trainalso leads to a decrease in power consumption, thereby prolongingbattery life and reducing battery size.

The repeating non-regular (i.e., not constant) pulse patterns or trainscan take a variety of different firms. For example, as will be describedin greater detail later, the inter-pulse intervals can be linearlycyclically ramped over time in non-regular temporal patterns (growinglarger and/or smaller or a combination of each over time); or beperiodically embedded in non-regular temporal patterns comprisingclusters or groups of multiple pulses (called n-lets), wherein n is twoor more. For example, when n=2, the n-let can be called a doublet; whenn=3, the n-let can be called a triplet; when n=4, the n-let can becalled a quadlet; and so on. The repeating non-regular pulse patterns orrains can comprise combinations of single pulses (called singlets)spaced apart by varying non-regular inter-pulse intervals and n-letsinterspersed among the singlets, the n-lets themselves being spacedapart by varying non-regular inter-pulse intervals both between adjacentn-lets and between the n pulses embedded in the n-let. If desired, thenon-regularity of the pulse pattern or train can be accompanied byconcomitant changes in waveform and/or amplitude, and/or duration ineach pulse pattern or train or in successive pulse patterns or trains.

Each pulse comprising a singlet or imbedded in an n-let in a given traincomprises a waveform that can be monophasic, biphasic, or multiphasic.Each waveform possesses a given amplitude (expressed, e.g., in amperesor volts) that can, by way of example, range from 10 μa (E⁻⁶) to 10 ma(E⁻³). The amplitude of a given phase in a waveform can be the same ordiffer among the phases. Each waveform also possesses a duration(expressed, e.g., seconds) that can, by way of example, range from 10 μs(E⁻⁶) to 2 ms (E⁻³). The duration of the phases in a given waveform canlikewise be the same or different. It is emphasized that all numericalvalues expressed herein are given by way of example only. They can bevaried, increased or decreased, according to the clinical objectives.

When applied in deep brain stimulation, it is believed that repeatingstimulation patterns or trains applied with non-regular inter-pulseintervals can regularize the output of disordered neuronal firing, tothereby prevent the generation and propagation of bursting activity witha lower average stimulation frequency than required with conventionalconstant frequency trains, i.e., with a lower average frequency thanabout 100 Hz.

FIG. 3 shows a representative example of a repeating non-regular pulsepattern or train in which the inter-pulse intervals are linearlycyclically ramped over time. As shown in FIG. 3, the pulse pattern ortrain includes singlet pulses (singlets) spaced apart by progressivelyincreasing inter-pulse intervals providing a decrease in frequency overtime, e.g., having an initial instantaneous frequency of 140 Hz,decreasing with doubling inter-pulse intervals, to a final instantaneousfrequency of 40 Hz. The inter-pulse intervals can vary within aspecified range selected based upon clinical objectives, e.g., not toexceed 25 ms, or not to exceed 100 ms, or not to exceed 200 ms, to takeinto account burst responses and subsequent disruption of thalamicfidelity). The non-regular pulse trains repeat themselves fir aclinically appropriate period of time. As shown in FIG. 3, the firstpulse train comprises progressively increasing inter-pulse intervalsfrom smallest to largest, followed immediately by another essentiallyidentical second pulse train comprising progressively increasinginter-pulse intervals from smallest to largest, followed immediately byan essentially identical third pulse train, and so on. Therefore,between successive pulse trains, there is an instantaneous change fromthe largest inter-pulse interval (at the end of one train) to thesmallest inter-pulse interval (at the beginning of the next successivetrain). The train shown in FIG. 3 has an average frequency of 85 Hz andis highly non-regular, with a coefficient of variation (CV) of about0.5. As is demonstrated in the following Example (Batch 3), theincreased efficiency of the pulse train shown in FIG. 3 (due to thelower average frequency) also can provide greater efficacy, as comparedto a constant 100 Hz pulse pattern.

The train shown in FIG. 3 exploits the dynamics of burst generation inthalamic neurons. The early high frequency phase of the train masksintrinsic activity in subthalamic nucleus (STN) neurons, and theinter-pulse interval increases reduce the average frequency. A family oftrains can be provided by varying the initial frequency, finalfrequency, and rate of change within the train, with the objective toprevent thalamic bursting with a lower average stimulation frequencythan required with constant frequency trains.

FIGS. 4 and 5 show other representative examples of repeatingnon-regular pulse patterns or trains. The pulse trains in FIGS. 4 and 5comprise within, a single pulse train, a combination of single pulses(singlets) and embedded multiple pulse groups (n-lets), with non-regularinter-pulse intervals between singlets and n-lets, as well asnon-regular inter-pulse intervals within the n-lets themselves. Thenon-regular pulse trains repeat themselves for a clinically appropriateperiod of time.

The non-regular pulse train can be characterized as comprising one ormore singlets spaced apart by a minimum inter-pulse singlet interval andone or more n-lets comprising, for each n-let, two or more pulses spacedapart by an inter-pulse interval (called the “n-let inter-pulseinterval”) that is less than the minimum singlet inter-pulse interval.The n-let inter-pulse interval can itself vary within the train, as canthe interval between successive n-lets or a successive n-lets andsinglets. The non-regular pulse trains comprising singlets and n-letsrepeat themselves for a clinically appropriate period of time.

In FIG. 4, each pulse train comprises four singlets in succession (withnon-regular inter-pulse intervals there between); followed by fourdoublets in succession (with non-regular inter-doublet pulse intervalsthere between and non-regular inter-pulse intervals within each n-let);followed by a singlet, three doublets, and a singlet (with non-regularinter-pulse intervals there between and non-regular inter-pulseintervals within each n-let). The temporal pattern of this pulse trainrepeats itself in succession for a clinically appropriate period oftime. The non-regular temporal pulse pattern shown in FIG. 4 has anaverage frequency of 67.82 Hz without loss of efficacy.

In FIG. 5, each pulse train comprises four singlets in succession (withnon-regular inter-pulse intervals there between); followed by threedoublets in succession (with non-regular inter-doublet pulse intervalsthere between and non-regular inter-pulse intervals within each n-let).The temporal pattern of this pulse train repeats itself in successionfor a clinically appropriate period of time. The non-regular temporalpulse pattern shown in FIG. 5 has an average frequency of 87.62 Hzwithout loss of efficacy.

Computational models of thalamic DBS and subthalamic DBS can be usedwith genetic-algorithm-based optimization (GA) to design non-regularstimulation patterns or trains that produce desired relief of symptomswith a lower average stimulation frequency than regular, high-ratestimulation. McIntyre et al. 2004 (Appendix A, hereto), Birdno, 2009(Appendix B, hereto); Rubin and Terman, 2004 (Appendix C, hereto); andDavis L (1991) Handbook of genetic algorithms, Van Nostrand Reinhold,NY, are incorporated herein by reference.

Possible mechanisms at the cellular and systems level may explain theeffectiveness using non-regular patterns of stimulation for thetreatment of patients with neurological disorders. At a cellular levelthe use of non-regular stimulation of the nervous system may rely on thepossibility that neurons are sensitive to the specific timing of thestimulation pulses. In other words, if the specific timing of thestimulation is important to individual neurons or even a population ofneurons, it may be advantageous for DBS systems to use non-regulartemporal patterns of stimulation to exploit this sensitivity and/orreactivity. In the branch of neuroscience concerned with the neural code(i.e. how neurons communicate information with one another) theimportance of the timing of inputs to a neuron as it relates toinformation transfer in the system is a common idea that is termedtemporal (or spatiotemporal) coding. At a systems level, a non-regularstimulation pattern could be more effective than regular stimulation atdisrupting or reversing pathological features of a neurological disordersuch as Parkinson's disease. For example, a non-regular pattern ofstimulation may be able effectively to break up pathologicalsynchronization and oscillations that are common in systems affected byPD. Exploiting the neural coding by taking advantage of the brain'ssensitivity, at any level, to the temporal structure of stimulationmakes the technology described herein different than any otherstimulation protocol ever developed to treat neurological disorders.

The technology described herein differs from prior systems and methodsby utilizing non-regular stimulation with a higher average frequency(greater than about 100 Hz, and preferably less than about 250 Hz) togain a clinical benefit greater than what can be elicited with regularhigh frequency stimulation.

While non-regular patterns of DBS have been tested in patients with PDin the past, the objective was to elucidate the mechanisms of DBS andthe importance of the pattern of stimulation for the efficacy of thetherapy. Results showed that the more non-regular you made randomlygenerated patterns of stimulation, the more ineffective that stimulationbecame at suppressing motor symptoms in Parkinson's disease patients(FIG. 6). It was not until more structured patterns of stimulationdesigned to expose the effects of certain characteristics of thestimulation were tested that non-regular, higher frequency patterns ofstimulation that were found to improve significantly a measure of motorperformance when compared to regular stimulation at a comparablefrequency (FIG. 15).

Others have proposed using non-regular patterns of stimulation(generated from non-linear dynamics) in mammals, and such methods seemto be effective in a mouse model of a minimally conscious state. Whilesuch results may be interesting, they are not in human patients, and thestimulation patterns were generated through different means. Indeed,results in human patients with ET and in human patients with PD showthat such random patterns of stimulation are not effective in relievingsymptoms. Patterns of stimulation according to the present invention aregenerated in a different way and are preferably structured andrepeating. It has been found that features of non-regular patterns ofDBS may need to be carefully chosen for the treatment of a specificneurological disorder in order to have the desired effects. Forinstance, a stimulation pattern that works for the treatment of PD maynot be efficacious in treating essential tremor (ET) and/or vice versa.

Stimulation pulses and methods according to the present invention may beimplemented in an implantable pulse generator capable of producingdesirable patterns of the non-regular stimulation. Known DBS devices, orsimilar variations thereof, may be used and programmed to generate thenovel stimulation patterns described here herein.

WORKING EXAMPLE

This invention has been used in treating or relieving symptoms ofParkinson's disease. The patterns of stimulation were designed to exposethe effects of certain characteristics of the stimulation and yieldednon-regular, high-frequency patterns of stimulation that significantlyimproved motor performance when compared to regular stimulation at acomparable frequency.

The way in which the non-regular patterns of stimulation were designedand/or configured for the present working example differentiates thepresent methodology from all previous work regarding electricalstimulation for the treatment of PD. The non-regular patterns ofstimulation were chosen because they contained features that may beimportant to the neural code in the DBS target area. These featuresincluded: bursts, pauses, gradual increments and/or decrements in theinterpulse interval, and other pulse structures thought to be importantfor communicating information between neurons in the brain.

In the PD example, after failing to find randomly generated non-regularpatterns of stimulation capable of increasing the efficacy of DBScompared to conventional regular pattern of stimulation, non-regularpatterns of stimulation were designed to elucidate the effects ofcertain characteristics of the stimulation pattern. For example, astimulation pattern was created, wherein such pattern included bursts ofstimulation pulses in rapid succession separated by groups of evenlyspaced stimulation pulses (see FIG. 7D). These novel patterns ofstimulation where tested using intraoperative experiments. Theseintraoperative experiments were conducted by connecting to an exposedlead of DBS electrodes implanted in a human, then delivering thepatterns of stimulation. Motor impairment was then quantified whiledelivering the patterns of stimulation using a finger-tapping task.

The results that certain of these trains or temporal patterns ofstimulation provided greater treatment of symptoms that regular highfrequency stimulation were unexpected. FIG. 6 shows priorexperimentation, which indicated that greater variability in DBSstimulation pulse trains resulted in increased motor symptom severity.The stimulation applied included randomly generated, gamma distributedinter-pulse intervals. Following such results, what was expected in thepresent implementation was that non-regular stimulation would worsenmotor symptoms.

FIGS. 7A-7D depict various non-regular stimulation patterns applied tohumans according to the present invention. The first stimulationpattern, in FIG. 7A, may be referred to as a Uniform temporallynon-regular stimulation. The Uniform stimulation pulse train includesnon-regular timing between stimulation pulses, but does not includestimulation bursts or pauses. As used herein, a stimulation pulse burstis defined as an occurrence of at least two consecutive instantaneouspulse frequencies (IPF's) (IPF_(i) and IPF_(i+1)) greater than2*IPF_(m), where IPF_(m) is the average IPF over some period of timepreceding IPF_(i), such as about 125 milliseconds. As used herein, astimulation pulse pause is defined as an IPF that is lower than adesired frequency, such as lower than the minimum frequency at which DBSeffectively suppresses tremor, which may be about 90 Hz. Another way ofexplaining a pulse pause is a desirable period of time, such as about 11milliseconds, that passes without the initiation of a stimulation pulse.The Uniform pulse train may be said to be characterized by a log-uniformdistribution of instantaneous pulse frequencies (IPFs).

FIG. 7B depicts what may be referred to as a Unipeak stimulation pulsetrain, which includes a wider log-uniform distribution of instantaneouspulse frequencies, including some pulse train bursts and some pulsetrain pauses.

FIG. 7C depicts a stimulation pulse train, which may be termed theAbsence train, which included a regular, periodic stimulation, butincluding pulse train pauses, but no pulse train bursts.

FIG. 7D shows another stimulation pulse train, which may be referred toas the Presence train, which included a regular, periodic stimulation,and further including pulse train bursts, but no pulse train pauses.

FIG. 8 provides a summary table of the properties of the above-discussedstimulation trains, as well as a Regular stimulation train of periodicstimulation provided at 185 Hz. In the table, MPR refers to mean pulserate, expressed in Hertz. Mean(IPF) is the mean instantaneous pulsefrequency, calculated by the following equation:

Σi=1n−1□(1/IPIi)n−1

where n equals the number of stimulation pulses in the pulse train, andIPI equals the inter-pulse interval, or time between the start of pulsenumber i and pulse number i+1 in the pulse train. Also in the table inFIG. 8, the coefficient of variation of the stimulation pulse trains'IPF and IPI is provided, where the coefficient of variation is definedby the standard deviation of the respective variable (IPF or IPI)divided by the mean of the respective variable.

Ten patients completed the experimental study and were included in thedata analysis. The table shown in FIG. 9 discloses some patient data. Inthe Target column of the table, STN refers to a target site ofstimulation including the patient's subthalamic nucleus and GPi refersto a target site of stimulation including the patient's globus pallidusinterna.

In the experimental study the Absence and Presence patterns were bothperiodic with low entropy (<1 bits/pulse) and characterized by eithershort periods absent of pulses or the presence of short bursts ofpulses, respectively. The pauses and bursts both occurred at 4.4 Hz. TheUniform and Unipeak patterns were highly irregular (high entropy:^(˜)5.5-5.6 bits/pulse) and were created from log-uniform distributionsof IPFs. Although the Unipeak pattern was created from a widerlog-uniform distribution of IPFs (44-720 Hz) than the Uniform pattern(90-360 Hz), the two patterns had the same entropy.

FIG. 10A provides the stimulation delivery and data collection timeline.Each black box rectangle indicates a period of four minutes during whicheither stimulation is turned off (DBS OFF) or turned on (DBS ON). Duringeach 4-minute window, data collection, as further described below,occurred during two time periods of twenty seconds each. First, at abouttwo minutes into the 4-minute window, data collection period “a”started, and second, at about three minutes and thirty seconds into the4-minute window, data collection period “b” started.

FIG. 10B provides an overview of which data was analyzed. First,baseline data was obtained. This data was taken from data collectiontime period “b” in the “Pre-Baseline” 4-minute window. Next, for eachpatient, the trial “b” data collected during DBS ON times was analyzedand compared to the baseline data. If a certain period of trial “b” datacollection was not completed, then trial “a” data was analyzed for thatwindow for that patient.

With reference to FIGS. 11-14, the data collection methodology may nowbe explained. In FIG. 11, previously conducted experiments using akeyboard and with reference to both hands, it was found that thecoefficient of variation of the time duration of the depression of a keyon a keyboard was statistically significantly correlated to motorsymptom severity. See Taylor Tavares, et al. (2005). To measure theeffect of the DBS stimulation patterns according to the presentinvention, a two-button computer mouse was utilized, and the patient wasinstructed to, during the data collection times, alternate clicking arespective mouse button with their index finger and their middle finger.The time duration of the respective button clicks was then recorded by acomputer and analyzed. Due to an observed greater variation in middlefinger click duration across patients, as shown in FIGS. 13A and B, datafrom index finger clicks was thought more reliable and thereforeanalyzed. That is, since the collected click time durations for theindex fingers and middle fingers were substantially differentlydistributed, the respective finger durations were not likely goodcandidates to be pooled for statistical analyses.

FIG. 14 depicts click duration data collected from a first patient (ontop) and a second patient (on bottom). As can be seen, during DBS OFFtimes, there was great variation in the click durations for each finger.Indeed, there is even substantial overlap with both fingers clicking themouse buttons at the same time. As can be seen on the right for eachpatient, when stimulation patterns according to the present inventionwere applied, improvements can be seen both in click durationconsistency, as well as reduced simultaneous clicking.

As demonstrated in FIG. 15, stimulation patterns and methods accordingto the present invention have been shown to increase the efficacy ofsuch stimulation, preferably without substantially increasing the meanfrequency of the stimulation over a generally accepted frequency range,and maintaining a constant geometric mean frequency. Smaller values onthe bar graph's y-axis indicate better performance on a motor taskexecuted during the application of the DBS patterns. Bars not labeledwith the same letter are significantly different from one another.

As indicated earlier, the results were unexpected. In priorexperimentation, greater variability in DBS stimulation correlated to agreater motor symptom severity. Not only were the results unexpected,but the results also cannot be explained with reference to generallyaccepted computer models that reflect expected behavior.

FIG. 16A depicts, on the left, a generally accepted computer model fromwhich thalamic neurological errors may be modeled. On the right, FIG.16A shows examples of such errors. First, a “miss” error 212 is shown.That is, when a sensorimotor input is provided to the thalamus, acorresponding thalamic neuron response is expected, but does not show.Next, a “burst” error 214 is shown. A burst error occurs when more thanone thalamic neuron response is generated in a short time window after asensorimotor input. Finally, a “spurious” error 216 is a thalamic neuronresponse that is generated without the thalamus receiving a sensorimotorinput.

In the experimental study the computer model is a biophysical model ofthe basal ganglia in a PD state including the STN, GPi, and externalglobus pallidus (GPe). Each nucleus of the basal ganglia model contains10 single compartment neurons. Each GPe neuron sends inhibitoryprojections to two STN neurons, two GPI neurons, and two other GPeneurons. STN neurons may send excitatory projections to two GPe neuronsand two GPi neurons. The biophysical properties of each neuron type werevalidated against experimental data and are described in detailelsewhere. Constant currents were applied to neurons in each nucleus torepresent inputs from afferent projections that were not included in themodel and produced firing rates that were consistent with observationsin non-human primate models of PD and human patients with PD. Forexample, STN and GPi neurons received applied current of 33 μA/cm² and21 μA/cm², respectively. Variability was added to the model bydelivering a constant current to each GPe neuron randomly drawn from anormal distribution centered around 8 μA/cm² with a standard deviationof 2 μA/cm². STN DBS was applied by delivering the desired pattern ofcurrent pulses (amplitude 300 μA/cm²; pulse width 0.3 ms) to each STNneuron.

As shown in FIG. 16B, DBS delivered to the model may reduce the errorfraction, as defined, along a stimulation frequency range between about100 Hz to about 200 Hz. This tuning curve of error fraction as afunction of DBS frequency in the biophysical model parallels stronglythe tuning curve of symptoms as a function of DBS frequency in patientswith PD.

The observed improvements (FIG. 15) by the application of stimulationaccording to the present invention cannot be explained by theconventional wisdom embodied in the generally accepted computer model ofthalamic response. By delivering the stimulation trains to the model,expected values are generated, as can be seen in FIGS. 17A-C. FIG. 17Ashows average error fraction data generated by a generally acceptedcomputer model. A lower average error fraction would seem to indicate anexpected lower motor symptom severity as measured by click duration. Ascan be seen, the Regular stimulation pattern would be expected togenerate a lower motor symptom severity than the patterns according tothe present invention. However, as explained above and with reference toFIG. 15, the stimulation patterns according to the present inventionperformed better.

Also, stimulation patterns according to the present invention wereexpected to perform worse than previous Regular DBS trains based on ananalysis of expected beta band oscillations generated by the model, asseen in FIG. 17B. There is some conventionally accepted correlationbetween beta band oscillations and slower motor response. That is, anincreased strength or power of beta band oscillations is generallycorrelated to a higher motor symptom severity, or slower motor response.Examining expected beta band oscillations from the model, the priorRegular stimulation patterns would be expected to perform better thanthe stimulation patterns according to the present invention. However, asexplained above and with reference to FIG. 15, the stimulation patternsaccording to the present invention performed better.

Furthermore, the success of the stimulation pattern trains according tothe present invention does not appear to be explainable or correlated tothe types of errors expected, or as generated by the model, as seen inFIG. 17C.

Thus, conventional experiments and associated wisdom as embodied ingenerally accepted models all predicted that stimulation pattern trainsaccording to the present invention would fail, or at least perform worsethan conventional Regular DBS stimulation patterns. In the end, however,it was found that stimulation pattern trains according to the presentinvention performed better than prior trains.

Further, post-hoc testing also revealed significant differences betweenstimulation patterns. During Absence, Presence, and Uniform DBS, the tapduration variability, a validated measure of symptom severity, was lowerthan during Regular DBS, indicating that these patterns improvedbradykinesia in PD more effectively than the temporally regularstimulation pattern used clinically. Motor task performance (Log CVDuration) during the Unipeak and Regular patterns was similar, see FIG.15. Consequently, tap duration variability during the Absence, Presence,and Uniform stimulation patterns was tower than during the Unipeakpattern. When individually added to the repeated measures ANOVAstatistical model, there was not a significant effect of surgicaltarget, medication state, sedation status, or switching to a bipolarelectrode configuration.

The responses to the different temporal patterns of stimulation wereconsistent across subjects. In 9/10 subjects, motor performance wasbetter during the Absence and Uniform patterns compared to the Regularpattern. Motor performance was superior during Presence DBS compared toRegular stimulation in 7/10 subjects. Motor performance was improvedduring stimulation compared to Baseline in 80-100% of the subjectsdepending on the pattern.

Motor performance during the stimulation patterns was weakly correlatedwith motor performance during the preceding stimulation off period, seeFIG. 18A. This suggested that changes in finger tap duration variabilitybetween stimulation patterns were caused by the stimulation patternsthemselves, and were not a reflection of fluctuations in baseline motorperformance.

Instead, and consistent with the time course of the action of DBS in PD,motor performance during the stimulation off period following eachstimulation pattern reflected the motor performance during the precedingpattern of stimulation, as demonstrated by significant correlationsbetween finger tap duration variability during the stimulation patternand during the subsequent stimulation off periods, see FIG. 18B.

The log-transformed coefficient of variation of the intervals betweenfinger taps (log CV Interval) exhibited the same pattern of motorperformance across stimulation patterns as log CV Duration, See FIG.19A. The finger tap timing was the most irregular, on average, duringBaseline and the Unipeak pattern of stimulation, and the average log CVInterval during Absence, Presence, and Uniform DBS was lower than it wasduring Regular DBS. The log-transformed rate of finger tapping exhibiteda similar dependence on stimulation pattern. The fewest button pressesoccurred during Baseline (stimulation off), and the most occurred duringthe Presence pattern of stimulation, see FIG. 19B.

It was discovered that some temporal patterns of DBS improved motorperformance more than regular stimulation, but there was also a desireto determine which features of the stimulation patterns influenced theefficacy of DBS. Therefore, the effects of bursts, pauses, andirregularity in the stimulation patterns were evaluated by pooling motorperformance data across stimulation trains that shared the feature ofinterest. Data during Presence and Unipeak DBS were pooled into a“Bursts” group and the remaining patterns into a “No Bursts” group;measurements made during Absence and Unipeak DBS were pooled into the“Pauses” group; and measurements from Uniform and Unipeak DBS werepooled into the “Irregular” group.

Quantitative measurement of the effects of different temporal patternsof DBS on bradykinesia in subjects with PD and oscillatory activity ofmodel neurons revealed three central findings. First, the pattern ofstimulation, and not simply the stimulation rate, was an importantfactor in the clinical efficacy of DBS, as demonstrated by the differentlevels of performance on a simple motor task during different temporalpatterns of stimulation all of which had the same mean frequency.Second, some non-regular patterns of stimulation relieved motor symptomsin PD more effectively than the temporally regular stimulation patternused clinically. Third, the differential efficacy of DBS patterns wasstrongly correlated with the pattern's ability to suppress beta bandoscillatory activity in a computational model of the basal ganglia.

The correlations between log CV Durations and the bradykinesia andrigidity UPDRS motor subscores are significant, but it remains unclearwhether these non-regular patterns of stimulation would ameliorate otherparkinsonian motor signs. UPDRS motor score improvements acrossstimulation patterns were predicted from log CV Duration values usingthe correlation between these two variables, see FIG. 20. Changes in logCV Duration from Baseline for each patient were multiplied by thecorrelation coefficient (R=0.58) and scaled by the gain (80 UPDRS motorpoints per 0.75 log unit) to predict stimulation-induced shifts in UPDRSmotor scores across stimulation patterns. The difference ill log CVDuration scores between Regular stimulation and Absence, Presence, andUniform patterns represented an improvement of 12-15 UPDRS motor scorepoints on average, suggesting that these temporal patterns ofstimulation provide clinically meaningful improvement over temporallyregular stimulation.

The present invention shows that different temporal patterns of DBSdifferentially suppressed oscillatory activity in a computational modelof the basal ganglia. FIG. 21A shows spectrograms of GPi spike timesfrom the computational model of the basal ganglia in the PD state acrossstimulation conditions. FIG. 21B shows that changes in beta bandoscillatory power during delivery in the biophysical model of differenttemporal patterns of DBS were strongly correlated with changes insymptoms when the same patterns of stimulation were delivered to humanpatients with PD. FIG. 21C shows the correlation between the log CVDuration and beta power in arbitrary units.

Oscillatory and synchronized neural activity specific frequency bandsappear to be related to motor performance in patients with PD, and thenon-regular patterns of stimulation that were most effective may be mostable to override or otherwise disrupt pathological oscillations orsynchronization in the basal ganglia. Indeed, the degree of suppressionof the oscillatory activity in the model neurons matched the clinicalefficacy of the patterns during the finger tapping task remarkably well,suggesting that the efficacy of these patterns of DBS depended on theirability to suppress, disrupt, or otherwise regularize pathologicalactivity in the basal ganglia.

In using previous systems and/or methods, the frequency or the amplitudeof the DBS is increased when a patient or clinician desires a morepronounced effect from the stimulation. Unfortunately, this inevitablyleads to a shorter battery life for the implantable pulse generatorsystem because of the higher demands placed on it. This calls for morefrequent battery recharging or surgery to replace non-rechargeableimplantable pulse generator. Instead of only increasing the intensity(amplitude or frequency) of stimulation and reaping the consequences ofthose actions, it is beneficial to increase the efficacy of thestimulation by simply changing the pattern of stimulation. That isexactly what the technology described in this invention does. Itprovides a greater level of symptom suppression for the patient whileusing an average frequency of stimulation similar to frequenciespreviously used in standard practice.

It is contemplated that non-regular stimulation patterns or trains canbe readily applied to deep brain stimulation, to treat a variety ofneurological disorders, such as Parkinson's disease, movement disorders,epilepsy, and psychiatric disorders such as obsessive-compulsiondisorder and depression. The non-regular stimulation patterns or trainscan also be readily applied to other classes electrical stimulation ofthe nervous system including, but not limited to, cortical stimulation,spinal cord stimulation, and peripheral nerve stimulation (includingsensory and motor), to provide the attendant benefits described aboveand to treat diseases such as but not limited to Parkinson's Disease,Essential Tremor, Movement Disorders, Dystonia, Epilepsy, Pain,psychiatric disorders such as Obsessive Compulsive Disorder, Depression,and Tourette's Syndrome.

It is contemplated that the systems and methodologies make it possibleto determine the effects of the temporal pattern of DBS on simulated andmeasured neuronal activity, as well as motor symptoms in both animalsand humans. The methodologies make possible the qualitativedetermination of the temporal features of stimulation trains.

According to the systems and methods according to the present invention,it has further been demonstrated that stimulation having a pattern,preferably a repeating pattern, of non-regular stimulation at a highaverage frequency may increase the efficacy of electrical stimulationprovided to relieve symptoms of neurological disorders, such as thosetreated with DBS. A system or method according to the present inventionmay generate or utilize a higher frequency (about 100 to about 200Hertz) non-regular pattern of DBS for the treatment or alleviation ofsymptoms of neurological disorders.

The foregoing is considered as illustrative only of the principles ofthe invention. Furthermore, since numerous modifications and changeswill readily occur to those skilled in the art, it is not desired tolimit the invention to the exact construction and operation shown anddescribed. While the preferred embodiment has been described, thedetails may be changed without departing from the invention, which isdefined by the claims.

INCORPORATED APPENDICES

-   McIntyre C C, Grill W M, Sherman D L, Thakor N V (2004) Cellular    effects of deep brain stimulation: model-based analysis of    activation and inhibition. J Neurophysiol 91:1457-1469, incorporated    as Appendix A, hereto.-   Birdno M J “Analyzing the mechanisms of thalamic deep brain    stimulation: computational and clinical studies”. Ph.D.    Dissertation. Department of Biomedical Engineering, Duke University,    Durham, N.C., USA, August 2009, incorporated as Appendix B, hereto.-   Rubin J E, Terman D (2004) High frequency stimulation of the    subthalamic nucleus eliminates pathological thalamic rhythmicity in    a computational model. J Comput Neurosci 16:211-235, incorporated as    Appendix C, hereto.-   Brocker D T, Swan B D, Turner D A, Gross R E, Tatter S B, Koop M M,    Bronte-Stewart H, Grill W M “Improved Efficacy of Temporally    Non-Regular Deep Brain Stimulation in Parkinson's Disease”    Department of Biomedical Engineering, Duke University, Durham, N.C.,    USA, incorporated as Appendix D, hereto.

OTHER REFERENCES

-   Dorval, A. D., A. M. Kuncel, et al. (2010). “Deep Brain Stimulation    Alleviates Parkinsonian Bradykinesia by Regularizing Palladial    Activity.” J Neurophysiol 104(2):911-921.-   Feng X J, Shea-Brown E, Greenwald B, Kosut R. Rabitz H (2007)    Optimal deep brain stimulation of the subthalamic nucleus—a    computational study. J Comput Neurosci. 23(3):265-282.-   Grefenstette J J (1986) Optimization of Control Parameters for    Genetic Algorithms. IEEE Transactions on Systems, Man and    Cybernetics 16:122-128.-   Taylor Tavares, A. L., G. S. X. E. Jefferis, et al. (2005).    “Quantitative measurements of alternating finger tapping in    Parkinson's disease correlate with UPDRS motor disability and reveal    the improvement in fine motor control from medication and deep brain    stimulation.” Movement Disorders 20(10): 1286-1298.

Having thus described the invention, we claim:
 1. A coupler assemblycomprising: a trailer pivot capable of engagement with a towed vehicle;an extension arm attached to the trailer pivot; a vehicle engagementmember capable of selectively engaging a hitch ball mounting aperture ofa towing vehicle; an engaging device secured to the extension arm andattached to the vehicle engagement member; and a pivot retention memberengaged with the engaging device generally preventing rotation of thetowing vehicle relative to the extension arm.
 2. The coupler assembly ofclaim 1, wherein the trailer pivot includes a generally vertical axiswhereby the towed vehicle may rotate relative to the vertical axis. 3.The coupler assembly of claim 2, wherein the towed vehicle is capable oftilting relative to towing vehicle at the vehicle engagement member. 4.The coupler assembly of claim 3, wherein the towed vehicle is capable oftilting fore and aft relative to the vertical axis at the vehicleengagement member.
 5. The coupler assembly of claim 1, wherein thevehicle engagement member includes a mounting body shaped and sized tobe secured to the hitch ball mounting aperture of the towing vehicle. 6.The coupler assembly of claim 5, wherein the engaging device includes amounting block and a generally U-shaped bracket attached to the mountingbody.
 7. The coupler assembly of claim 6, wherein the pivot retentionmember includes a mounting pin attached to the generally U-shapedbracket and engageable with the mounting block generally preventingrotation of the towing vehicle relative to the extension arm.
 8. Thecoupler assembly of claim 7, wherein the mounting pin prevents rotationabout a first vertical axis at the mounting body.
 9. The couplerassembly of claim 8, wherein the trailer pivot is capable of rotationabout a second vertical axis.
 10. The coupler assembly of claim 1,wherein the vehicle engagement member includes a socket attached to theextension arm and a hitch ball engaged with the socket and attached tothe hitch ball mounting aperture of the towing vehicle.
 11. The couplerassembly of claim 10, wherein the engaging device includes a retentionarm attached to the socket.
 12. The coupler assembly of claim 11,wherein the pivot retention member includes a support plate attached tothe towing vehicle.
 13. The coupler assembly of claim 12, wherein theretention arm is generally aligned with a center of the hitch ball. 14.The coupler assembly of claim 13, wherein the retention arm is capableof engaging the support plate.
 15. The coupler assembly of claim 14,wherein the support plate includes a retention pocket capable ofengaging the retention arm.
 16. A coupler assembly comprising: a trailerpivot capable of engagement with a towed vehicle; an extension armattached to the trailer pivot; a vehicle engagement member capable ofbeing mounted to a hitch ball mounting aperture of a towing vehicle; anengaging device secured to the extension arm and attached to the vehicleengagement member; a first pivot retention member engaged with theengaging member generally preventing rotation of the towing vehiclerelative to the extension arm; and a second pivot retention memberattached to the vehicle engagement member and capable of engaging thetowing vehicle generally preventing rotation of the towing vehiclerelative to the extension arm.
 17. The coupler assembly of claim 16,wherein the vehicle engagement member includes a platform and the secondpivot retention member includes a pin capable of engaging the towingvehicle.
 18. The coupler assembly of claim 17, wherein the vehicleengagement member includes a socket attached to the extension arm and ahitch ball attached to the platform and engaged with the socket.
 19. Thecoupler assembly of claim 17, wherein the vehicle engagement memberincludes a mounting body attached to the platform.
 20. A couplerassembly comprising: a towed vehicle engagement member configured toattach with a towed vehicle, wherein the towed vehicle is capable ofpivoting around a first vertical axis with respect to the towed vehicleengagement member; an extension arm extending from the engagementmember; a towing vehicle engagement member capable of coupling with atowing vehicle, the towing vehicle engagement member coupled with theextension arm; and an engaging member coupled with the vehicleengagement member generally preventing movement of the towing vehicleengagement member with respect to a second vertical axis relative to thetowing vehicle.
 21. The coupler assembly of claim 20, wherein the firstand second vertical axes are generally parallel.